Building an ELT Pipeline in Python and Snowflake

Extracting, Loading and Transforming Data

Image by Natalia Bregano

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two processes used for integrating and transforming data, but they have different approaches. Think of it like cooking a meal — ETL is like preparing and chopping all the ingredients before cooking, while ELT is like cooking the ingredients first and then adding spices and seasonings to taste.

The key difference is that in ETL, data is transformed before being loaded into a data warehouse, whereas in ELT, the raw data is loaded into the warehouse first and then transformed using SQL or other data transformation tools.

Both ETL and ELT have their own advantages and disadvantages.

• Personally, I prefer ELT over ETL because it’s more flexible and makes it easier to store new, unstructured data. With ELT, you can save any type of information without having to transform and structure it first, which gives you immediate access to all your information.
• ELT is faster than ETL when it comes to data availability. With ELT, all the data can be immediately loaded into the system, and users can then decide which data they want to transform and analyze.
• ELT also requires less maintenance compared to ETL. Since…